Paper Citation Record · LEDGER

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model

As of 23 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2505.05049.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.05049 v4

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:18:53.786959Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-24T02:26:29.355957Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-24T02:28:46.073955Z

Reference resolution

48 of 48 outbound references displayed

verified exact1
verified fuzzy42
unresolved4
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 11c97325-8f82-4ecb-863d-b5b423f33ac0 · outbound

This paper cites write newline.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model write newline

Reference 1

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Source-reported events for the cited work

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Observation 27f64876-791d-4ac9-917b-a0afdfe65493 · outbound

This paper cites P., Mishra, S., Zhou, P., Gupta, A., Rajagopal, D., Kappaganthu, K., Yang, Y., Upadhyay, S., Faruqui, M., and ., M.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model P., Mishra, S., Zhou, P., Gupta, A., Rajagopal, D., Kappaganthu, K., Yang, Y., Upadhyay, S., Faruqui, M., and ., M

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.514520Z

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Observation 395fa18a-a953-464c-8615-16aa9bdbc736 · outbound

This paper cites Automatic image colorization via multimodal predictions.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Automatic image colorization via multimodal predictions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.500625Z

Source-reported events for the cited work

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Observation 849e944a-70c1-4b57-b1f3-f060f52c4caf · outbound

This paper cites SAM - U : Multi -box Prompts Triggered Uncertainty Estimation for Reliable SAM in Medical Image.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model SAM - U : Multi -box Prompts Triggered Uncertainty Estimation for Reliable SAM in Medical Image

Reference 4

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Observation 0cbc259c-2107-49e3-b4b8-c1962cb600b5 · outbound

This paper cites H., and Bai, S.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model H., and Bai, S

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.470157Z

Source-reported events for the cited work

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Observation fb56ab9a-3200-4102-ab6f-ab3871f8a75f · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model An image is worth 16x16 words: Transformers for image recognition at scale

Reference 6

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Source-reported events for the cited work

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Observation 5c5d215b-578a-4dd4-92b3-5f0538ee5880 · outbound

This paper cites and Ghahramani, Z.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model and Ghahramani, Z

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.447764Z

Source-reported events for the cited work

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Observation 5ef61193-88da-4d69-a9ff-73adb6005d96 · outbound

This paper cites and Ghahramani, Z.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model and Ghahramani, Z

Reference 8

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Observation 1e953c34-d0fc-4868-bf76-9b10ff1147c5 · outbound

This paper cites A survey of uncertainty in deep neural networks.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model A survey of uncertainty in deep neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.419202Z

Source-reported events for the cited work

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Observation da488153-a087-4a02-8ae3-d8e2d3e8f347 · outbound

This paper cites Hypersparse neural networks: Shifting exploration to exploitation through adaptive regularization.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Hypersparse neural networks: Shifting exploration to exploitation through adaptive regularization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.405362Z

Source-reported events for the cited work

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Observation d8f82eaa-48e8-4675-a103-ae8c648b331b · outbound

This paper cites and Fookes, C.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model and Fookes, C

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.390175Z

Source-reported events for the cited work

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Observation e26c9860-7ef7-4fca-845b-5f7df29bed75 · outbound

This paper cites Safe resetless reinforcement learning: Enhancing training autonomy with risk-averse agents.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Safe resetless reinforcement learning: Enhancing training autonomy with risk-averse agents

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.375269Z

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Observation 89fa592f-77e4-4a2f-847a-2d2238f0b31c · outbound

This paper cites O., Schierholz, M., Kreuter, F., and Kauermann, G.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model O., Schierholz, M., Kreuter, F., and Kauermann, G

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.361804Z

Source-reported events for the cited work

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Observation 8933a4e3-67ce-43a9-b240-d0ae118c932a · outbound

This paper cites Multiple choice learning: Learning to produce multiple structured outputs.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Multiple choice learning: Learning to produce multiple structured outputs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.347622Z

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Observation db5dcc6c-48f8-4ef5-8d25-a6e08eb8cbd9 · outbound

This paper cites \'E tude comparative de la distribution florale dans une portion des alpes et des jura.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model \'E tude comparative de la distribution florale dans une portion des alpes et des jura

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.332462Z

Source-reported events for the cited work

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Observation 75e86f09-7366-42db-8076-53b7826ca769 · outbound

This paper cites Uncertainty-aware adapter: Adapting segment anything model (sam) for ambiguous medical image segmentation.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Uncertainty-aware adapter: Adapting segment anything model (sam) for ambiguous medical image segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.318736Z

Source-reported events for the cited work

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Observation 39ec08f4-6b91-4cf9-a99a-3199aa013bc0 · outbound

This paper cites Subjective Logic: A formalism for reasoning under uncertainty.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Subjective Logic: A formalism for reasoning under uncertainty

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.304063Z

Source-reported events for the cited work

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Observation 2106e376-9303-4c80-a949-0cfc660a2a6a · outbound

This paper cites Blind knowledge distillation for robust image classification.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Blind knowledge distillation for robust image classification

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.290476Z

Source-reported events for the cited work

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Observation ffc2dede-5aea-4aad-b892-cdf85fb08aad · outbound

This paper cites Compensation learning in semantic segmentation.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Compensation learning in semantic segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.276868Z

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Observation bc49a1c9-40be-4f2e-9be5-95afb0e5167a · outbound

This paper cites Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:18:53.849497Z

Source-reported events for the cited work

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Observation 401b42dc-ccf9-4665-9bf8-82e26a5d0bf2 · outbound

This paper cites Position: Uncertainty quantification needs reassessment for large language model agents.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Position: Uncertainty quantification needs reassessment for large language model agents

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.262366Z

Source-reported events for the cited work

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Observation cbbbfe46-4a3c-47a3-aa00-78dfa8b90d2c · outbound

This paper cites C., Lo, W.-Y., et al.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model C., Lo, W.-Y., et al

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.248025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e3e9b34a-4456-4344-8867-3ac30167206c · outbound

This paper cites and Rosenhahn, B.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model and Rosenhahn, B

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.232477Z

Source-reported events for the cited work

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Observation 3395bbbe-3ced-4e87-913e-b42b799bfc7b · outbound

This paper cites I., Bertin, P., Rector-Brooks, J., Korablyov, M., and Bengio, Y.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model I., Bertin, P., Rector-Brooks, J., Korablyov, M., and Bengio, Y

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.217912Z

Source-reported events for the cited work

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Observation 516212fd-e565-4551-a823-512d14c09efc · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.202137Z

Source-reported events for the cited work

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Observation 6f5c7662-e1d9-475b-b956-ba9ee9eec9fb · outbound

This paper cites Flaws can be applause: Unleashing potential of segmenting ambiguous objects in sam.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Flaws can be applause: Unleashing potential of segmenting ambiguous objects in sam

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.184438Z

Source-reported events for the cited work

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Observation c60bdf7f-d212-4751-991a-e59b44dca515 · outbound

This paper cites L., and Dollár, P.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model L., and Dollár, P

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.166398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4bd14819-c610-4714-810c-8201670b147b · outbound

This paper cites Smac3: A versatile bayesian optimization package for hyperparameter optimization.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Smac3: A versatile bayesian optimization package for hyperparameter optimization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.149788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ad595df8-48ef-40d4-ba3f-ce1bcc6705d1 · outbound

This paper cites Uncertainty-aware fine-tuning of segmentation foundation models.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Uncertainty-aware fine-tuning of segmentation foundation models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.133134Z

Source-reported events for the cited work

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Observation 12098ece-51e8-414e-b133-6e76f7ed98a8 · outbound

This paper cites an unresolved cited work.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Unresolved cited work

Reference 30

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Observation f95397b2-6778-4b28-80f2-8b6d15fc0706 · outbound

This paper cites Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation

Reference 31

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Observation 2cf6da05-b634-419c-9070-63e6577d16e8 · outbound

This paper cites A benchmark dataset and evaluation methodology for video object segmentation.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model A benchmark dataset and evaluation methodology for video object segmentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.101413Z

Source-reported events for the cited work

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Observation ac667872-0ec4-4d4f-b2f1-02bb1516f654 · outbound

This paper cites Deep-learning uncertainty estimation for data-consistent breast tomosynthesis reconstruction.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Deep-learning uncertainty estimation for data-consistent breast tomosynthesis reconstruction

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.086357Z

Source-reported events for the cited work

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Observation adad2d63-e5b7-4ed5-83e3-e30a515e0d72 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 34

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Source-reported events for the cited work

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Observation 6c63f877-5511-42b1-a743-9285f8624f97 · outbound

This paper cites V., Carion, N., Wu, C.-Y., Girshick, R., Doll \'a r, P., and Feichtenhofer, C.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model V., Carion, N., Wu, C.-Y., Girshick, R., Doll \'a r, P., and Feichtenhofer, C

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.062034Z

Source-reported events for the cited work

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Observation 46782598-57ae-44a0-b409-3eec616ee6b4 · outbound

This paper cites M., Bradbury, K., and Malof, J.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model M., Bradbury, K., and Malof, J

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.046634Z

Source-reported events for the cited work

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Observation d0addbc7-6504-468b-9f4e-05982c0b9d56 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Dropout: a simple way to prevent neural networks from overfitting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.030004Z

Source-reported events for the cited work

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Observation 7fc7b711-1938-4824-9c20-856e641ba2a8 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Fourier features let networks learn high frequency functions in low dimensional domains

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.014538Z

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Observation ff58239c-87e5-45a6-a684-184a99b53595 · outbound

This paper cites Strike the balance: On-the-fly uncertainty based user interactions for long-term video object segmentation.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Strike the balance: On-the-fly uncertainty based user interactions for long-term video object segmentation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:54.000772Z

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Observation cf18b7d4-c390-4d1f-b3a3-8b7c7ac3203f · outbound

This paper cites Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.986201Z

Source-reported events for the cited work

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Observation 4b439738-dcdb-4513-bd11-e1ce5aff7607 · outbound

This paper cites Utilizing uncertainty in 2d pose detectors for probabilistic 3d human mesh recovery.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Utilizing uncertainty in 2d pose detectors for probabilistic 3d human mesh recovery

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.971159Z

Source-reported events for the cited work

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Observation b18f5220-dd77-4fa0-a7f1-ef4e6e911a14 · outbound

This paper cites and Xu, M.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model and Xu, M

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.956876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e26b3fbf-b5ea-40fc-b4e5-539171ac1d51 · outbound

This paper cites Eviprompt: A training-free evidential prompt generation method for adapting segment anything model in medical images.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Eviprompt: A training-free evidential prompt generation method for adapting segment anything model in medical images

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.942888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3f86f19d-c3b3-43ce-843a-59e0c9d9b969 · outbound

This paper cites Segment-anything models achieve zero-shot robustness in autonomous driving.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Segment-anything models achieve zero-shot robustness in autonomous driving

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.926669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1817cf0c-1351-42ff-95f8-4287a12b31e7 · outbound

This paper cites Biomedical SAM -2: Segment anything in biomedical images and videos.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Biomedical SAM -2: Segment anything in biomedical images and videos

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.911248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d4c380bb-d920-414f-8ba4-15f5cd0aa021 · outbound

This paper cites A comprehensive survey on segment anything model for vision and beyond.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model A comprehensive survey on segment anything model for vision and beyond

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.896236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation adef921a-7d6a-48f6-a6a1-177f431b4a74 · outbound

This paper cites Segment anything model with uncertainty rectification for auto-prompting medical image segmentation.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Segment anything model with uncertainty rectification for auto-prompting medical image segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.880807Z

Source-reported events for the cited work

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Observation ca38fdc1-e775-485f-99aa-e1f0f84b99a3 · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset.

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model Semantic understanding of scenes through the ade20k dataset

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:18:53.865140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation f803218b-b0ef-4ffa-9d86-70c30f33aaf5 · inbound

Uncertainty Quantification on Graph Learning: A Survey cites this paper.

Uncertainty Quantification on Graph Learning: A Survey UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:28:46.077286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.